Temperature-Dependent Adsorption and Adsorption Hysteresis of a Thermoresponsive Diblock Copolymer
Bibliographic record
Abstract
A nonionic-cationic diblock copolymer, poly(2-isopropyl-2-oxazoline)60-b-poly((3-acrylamidopropyl)trimethylammonium chloride)17, (PIPOZ60-b-PAMPTMA17), was utilized to electrostatically tether temperature-responsive PIPOZ chains to silica surfaces by physisorption. The effects of polymer concentration, pH, and temperature on adsorption were investigated using quartz crystal microbalance with dissipation monitoring and ellipsometry. The combination of these two techniques allows thorough characterization of the adsorbed layer in terms of surface excess, thickness, and water content. The high affinity of the cationic PAMPTMA17 block to the negatively charged silica surface gives rise to a high affinity adsorption isotherm, leading to (nearly) irreversible adsorption with respect to dilution. An increase in solution pH lowers the affinity of PIPOZ to silica but enhances the adsorption of the cationic block due to increasing silica surface charge density, which leads to higher adsorption of the cationic diblock copolymer. Higher surface excess is also achieved at higher temperatures due to the worsening of the solvent quality of water for the PIPOZ block. Interestingly, a large hysteresis in adsorbed mass and other layer properties was observed when the temperature was cycled from 25 to 45 °C and then back to 25 °C. Possible causes for this temperature hysteresis are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".